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CHAPTER ONE

1.0 INTRODUCTION

Timely identification of newly emerging trends is very important to businesses. Sales patterns of

customer segments indicate market trends. Upward and downward trends in sales signify new

market trends. Time-seriespredictive modeling can be used to identify market trends embedded

in changes of sales revenues.

Predictive analytics encompasses a variety of techniques fromstatistics,modeling,machine

learning, anddata mining that analyze current and historical facts to makepredictions about

future, or otherwise unknown, events.

In business, predictive models exploitpatterns found in historical and transactional data to

identify risks and opportunities. Models capture relationships among many factors to allow

assessment of risk or potential associated with a particular set of conditions, guidingdecisionmaking for candidate transactions.

Predictive analytics is used inactuarial science,marketingfinancial

services,insurance,telecommunications,retail,

travel, healthcare,pharmaceuticals and other

fields.

One of the most well known applications iscredit scoring,which is used throughoutfinancial

services. Scoring models process a customer's credit history, loan application, customer data,

etc., in order to rank-order individuals by their likelihood of making future credit payments on

time

Predictive models analyze past performance to assess how likely a customer is to exhibit a

specific behavior in order to improvemarketing effectiveness.This category also encompasses

models that seek out subtle data patterns to answer questions about customer performance, such

as fraud detection models. Predictive models often perform calculations during live transactions,

for example, to evaluate the risk or opportunity of a given customer or transaction, in order to

guide a decision. With advancements in computing speed, individual agent modeling systems

have become capable of simulating human behaviors or reactions to given stimuli or scenarios.

The new term for animating data specifically linked to an individual in a simulated environment

is avatar analytics

Understanding of sales trends is important for marketing as well as for customer retention.

Regression is an analytic technique used in developing predictive models for numerical data. It

automatically derives mathematical functions that summarize trends embedded in past historical

data, in such a way that minimizes the errors between actual input data and predicted values by

the models. Regression can be applied to time-series data. A time-series consists of a set of

observations which are measured at specific time intervals, say, monthly, quarterly, yearly, etc.

Observations we are interested are sales revenues.
http://www.roselladb.com/predictive-modeling.htmhttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Predictive_modellinghttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/Predictionhttp://en.wikipedia.org/wiki/Pattern_detectionhttp://en.wikipedia.org/wiki/Decision_makinghttp://en.wikipedia.org/wiki/Decision_makinghttp://en.wikipedia.org/wiki/Actuarial_sciencehttp://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Insurancehttp://en.wikipedia.org/wiki/Telecommunicationshttp://en.wikipedia.org/wiki/Retailhttp://en.wikipedia.org/wiki/Travelhttp://en.wikipedia.org/wiki/Healthcarehttp://en.wikipedia.org/wiki/Pharmaceutical_companyhttp://en.wikipedia.org/wiki/Credit_scoringhttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Credit_historyhttp://en.wikipedia.org/wiki/Loan_applicationhttp://en.wikipedia.org/wiki/Marketing_effectivenesshttp://en.wikipedia.org/wiki/Marketing_effectivenesshttp://en.wikipedia.org/wiki/Loan_applicationhttp://en.wikipedia.org/wiki/Credit_historyhttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Credit_scoringhttp://en.wikipedia.org/wiki/Pharmaceutical_companyhttp://en.wikipedia.org/wiki/Healthcarehttp://en.wikipedia.org/wiki/Travelhttp://en.wikipedia.org/wiki/Retailhttp://en.wikipedia.org/wiki/Telecommunicationshttp://en.wikipedia.org/wiki/Insurancehttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Financial_serviceshttp://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Actuarial_sciencehttp://en.wikipedia.org/wiki/Decision_makinghttp://en.wikipedia.org/wiki/Decision_makinghttp://en.wikipedia.org/wiki/Pattern_detectionhttp://en.wikipedia.org/wiki/Predictionhttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Predictive_modellinghttp://en.wikipedia.org/wiki/Statisticshttp://www.roselladb.com/predictive-modeling.htm


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The analytical technique used for this research is the time series model. It is used for predicting

or forecasting the future behavior of variables. These models account for the fact that data points

taken over time may have an internal structure (such as autocorrelation, trend or seasonal

variation) that should be accounted for. As a result standard regression techniques cannot be

applied to time series data and methodology has been developed to decompose the trend,

seasonal and cyclical component of the series. Modeling the dynamic path of a variable can

improve forecasts since the predictable component of the series can be projected into the future.

Time series models estimate difference equations containing stochastic components. Two

commonly used forms of these models are autoregressive models (AR) andmoving

average (MA) models. TheBox-Jenkins methodology (1976) developed by George Box and

G.M. Jenkins combines the AR and MA models to produce the ARMA (autoregressive moving

average) model which is the cornerstone of stationary time series analysis.ARIMA

(autoregressive integrated moving average models) on the other hand are used to describe non-

stationary time series. Box and Jenkins suggest differencing a non stationary time series to obtain

a stationary series to which an ARMA model can be applied. Non stationary time series have apronounced trend and do not have a constant long-run mean or variance.

Box and Jenkins proposed a three stage methodology which includes: model identification,

estimation and validation. The identification stage involves identifying if the series is stationary

or not and the presence of seasonality by examining plots of the series, autocorrelation and

partial autocorrelation functions. In the estimation stage, models are estimated using non-linear

time series or maximum likelihood estimation procedures. Finally the validation stage involves

diagnostic checking such as plotting the residuals to detect outliers and evidence of model fit.

Nyce, Charles (2007),Predictive Analytics White Paper, American Institute for Chartered

Property Casualty Underwriters/Insurance Institute of America.

1.2 SCOPE AND LIMITATION

1.2.1 SCOPE

NBC operates the Apapa Plant since 1958 and is located in the Apapa area of Lagos State inSouth-West Nigeria. The Apapa plant is responsible for the production of Coca-Cola, Fanta,

Sprite and Schweppes and distribution of all product categories. Coca-Cola was an instant hit

with the Nigerian consumer and has remained so. Over the next six decades, NBC has continued

on its journey keeping its promise of refreshing consumers, strengthening its communities,

enriching the workplace and preserving the environment while recording many memorable

milestones along the way.
http://en.wikipedia.org/wiki/Autoregressive_modelhttp://en.wikipedia.org/wiki/Moving_average_modelhttp://en.wikipedia.org/wiki/Moving_average_modelhttp://en.wikipedia.org/wiki/Box-Jenkinshttp://en.wikipedia.org/wiki/Autoregressive_moving_average_modelhttp://en.wikipedia.org/wiki/Autoregressive_integrated_moving_averagehttp://www.aicpcu.org/doc/predictivemodelingwhitepaper.pdfhttp://www.aicpcu.org/doc/predictivemodelingwhitepaper.pdfhttp://www.aicpcu.org/doc/predictivemodelingwhitepaper.pdfhttp://www.aicpcu.org/doc/predictivemodelingwhitepaper.pdfhttp://en.wikipedia.org/wiki/Autoregressive_integrated_moving_averagehttp://en.wikipedia.org/wiki/Autoregressive_moving_average_modelhttp://en.wikipedia.org/wiki/Box-Jenkinshttp://en.wikipedia.org/wiki/Moving_average_modelhttp://en.wikipedia.org/wiki/Moving_average_modelhttp://en.wikipedia.org/wiki/Autoregressive_model


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1.2.2 LIMITATION

The process of getting the data for this research was quite cumbersome, demanding but on the

overall, its quite rewarding.

1.3 OBJECTIVES of the Study

1. To fit a model to the sales of each soft drink and forecast with it.

2. To compare the sales performance of the soft drinks

1.4 LITERATURE REVIEW

Some of the greatest contributors to the theme of the study are:

Christensen and Brogan (1971) in a journal on modeling and optimal control of a production

process disclosed that an industrial production process is modeled as a discrete-time system with

a disturbance input due to sales.. The state consists of rates of flow of parts or subassemblies at

various work stations, backlogs of parts awaiting processing, and the inventory level of the

finished product. The control variables are the man-hours scheduled for various work processes.

A quadratic performance criterion is minimized so as to keep state and control variables near

desired values. Dynamic programming is used and numerical examples are provided

Mandal (1980) in an article on vendor selection using interpretive structural modeling wrote that

Vendor selection is one of the most important activities of a purchasing department.

Traditionally, vendors are selected for their ability to meet the quality requirement, delivery

performance and the price offered. However, as they are selected not only to meet the immediate

requirement but also future needs, one needs to consider many other factors when selecting a

reliable vendor. Analyses some of the most important criteria which have been classified into

four categories: autonomous, dependent, linkage and driver depending on their driver power and

dependence. Develops an interpretive structural model (ISM) to show the inter-relationship of

different criteria and their levels of importance in the vendor selection process. Reveals thatattitude and willingness for business and after sales service are as important factors as

quality, delivery and practice. These criteria are dependent on all the others. This analysis could

provide a meaningful analytical base in the vendor selection process.

Betts et al (1996) in his article on Consumer behavior and the retail sales noted that In spite of

the ubiquity of seasonal and other retail sales, they have been curiously neglected within the


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marketing literature. This is most surprising, given their impact on profit-margins, brand/store

images, supplier-retailer relationships and consumer behavior. Since 1980, the effects of

comparison price advertising on consumer behavior have received growing attention from

researchers, although much of this literature has been at the individual product level and

confined to groceries. Builds on this research in the specific context of store wide sales, where

the impact of reductions extends far beyond the sum of individual price changes. Based on focus

groups and preliminary surveys of sale shoppers, develops and tests a typology of motivations.

Presents taxonomy of responses to sales, illustrating alternative behavioral responses.

Concludes with a model of the attitude problem brought about by recession, overcapacity,

overuse of the strategy and growing skepticism on the part of consumers.

Simon et al (1999) in an article on modeling of the cycle of products with data acquisition

features were of the opinion that Domestic appliances are long-lived and relatively expensive

products that come under the new EC electrical waste recovery directive. In the UK, washing

machines in particular tend to be unreliable; increasing the reuse of components could improve

the economics of end-of-life operations. Trends in appliance design are towards more

sophisticated control and networking; this makes adding functions to record data on the use of

the machines feasible. The data would also have value for life cycle environmental assessment.

The paper reports on the development of self-contained data acquisition units for washing

machines based on a microcontroller and non-volatile memory. The data has applications in

design, marketing and servicing as well as end-of-life. A batch of units have been manufactured

and tested on limited field trials in washing machines. Ten parameters are continuously

monitored, timed and/or recorded during appliance operation; error conditions are also logged

for use during servicing. The data is then downloaded, either during servicing or at end-of-life;

the dynamic data from use is combined with static data from manufacture. The information

system which links all parties interested in the data is the key aspect of life cycle data

acquisition. Two models are described which evaluate the economic benefits of adding such

functions to products: a steady-state model as used by previous authors who were concerned with

end-of-life product treatment and a more sophisticated transient model that accurately reflects the

limited life of designs. Results show that in this case, more reusable components arise from

servicing rather than from end-of-life recovery of parts. The cost savings from increased reuseare also estimated to be comparable to the additional cost of the system; greater savings could

well arise from the use of the use data in marketing.


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Heyse and Wei (2005) in a book on modeling the advertising-sales relationship through use of

multiple time series techniques were of the view that when time series data are available for both

advertising and sales, it may be worthwhile to model the two series jointly. Such an analysis may

contribute to our understanding of the dynamic relationships among the series and may improve

the accuracy of forecasts. Multiple time series techniques are applied to the well-known Lydia

Pinkham data to illustrate their use in modeling the advertising-sales relationship. In analyzing

the Lydia Pinkham data the need for a joint model is established and a bivariate model is

identified, estimated and checked. Its forecasting properties are discussed and compared to other

time series approaches.

Hoptroff (2005) in a journal on the principles and practice of time series forecasting and business

modeling using neural nets used a hands-on practical discussion of how and why neural

networks are used in forecasting and business modeling. The need for forecasting is briefly

examined. The theory of the multilayer preceptor neural network is then covered both

qualitatively and in mathematical detail, including the methods of back-propagation of error and

independent validation. The advantages of the neural net approach to forecasting, namely

nonlinear modeling capability, plausible interpolations and extrapolations, robustness to noise,

ill-conditioning and insufficient data, and ease of use, are discussed. Finally, some working notes

are offered for the practical implementation of neural nets in forecasting, and four real-life

examples are given from the pursuits of econometrics, sales forecasting, market modelling, and

risk evaluation.

Gupta et al (2005) in a journal on differential sales tax structure noted that it contributes

significantly to distribution network decisions that build logistics inefficiencies in firms

operating in India. In this paper, we develop a model for determining distribution centers (DCs)

locations considering the impact of CST. A non-linear mixed integer-programming problem that

is formulated initially is approximated to a mixed integer-programming problem. Using a

numeric example, the effect of CST rates and product variety on DC locations is studied and

found to be having impact. It is felt that the Indian Government proposal to switch over from the

present sales tax regime to value added tax (VAT) regime would significantly contribute to

reducing the logistics inefficiencies of Indian firms.

Burgess (2006) in a book on modeling business profitability noted that various methods are in

use for the top down assessment and comparison of the future profitability of businesses. The

most advanced is the PAR equation of the PIMS programme, which combines quantitative

evaluations of the average effect of various business characteristics on profitability. However it

is not a true model in the OR sense being essentially a linear regression equation. The proposed

new approach is a first attempt to combine the characteristics in a way which models the

bargaining of customers with suppliers who are in competition for the supply of a bulk product.

All the constants in the resulting non-linear equation (called 5C) are meaningful in marketing or

production terms. The model is in a preliminary non-optimized state, but seems to offer

opportunities for ongoing development. It is in this spirit that it is offered for discussion.


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Buzzell and Wiersema (2006) in a journal on modeling changes in market share noted that the

decision to build market share has major resource-allocation implications. To aid managers in

assessing these implications, research was conducted to determine general relationships between

changes in market share and variables representing market strategies and competitive position.

The research was based on multiproduct, cross-sectional regression analyses and includes

variables that are - or should be readily available to most businesses.

Chanda and Bardhan (2008) in a book on modeling innovation wrote that Majority of consumer

durables have multiple technological generations. Each succeeding generation offers some

innovative performance enhancements, feature additions etc. distinguishing itself from the past

releases. Therefore the consumer's attitude towards each of them can be very different. There is a

need to understand consumer psychology and have accurate measure to predict the adoption

process of new technology. Mathematical models have proved to be ideal tools to explain the

past purchasing-behavior and also for forecasting. This paper focuses on studying the relative

changes of diffusion parameters for both first time purchasers and upgraders along with

developing a more general sales model for multiple technology generation products. The

proposed model explicitly identifies different groups of purchaser viz. first timers and repeaters

(upgraders).

Themido et al (2006) in a journal on modeling the retail sale of gasoline described the modelling

approach for the sales of gasoline in service stations located in a Portuguese metropolitan area.

The models were developed for planning purposes in order to assess potential sales of new sites.

The estimates produced are more accurate than those made by previous models and are currently

being used to support investment decisions of the largest Portuguese company. Starting from a

basic multiplicative regression model, several segmentations of the service stations were

obtained. Among these, the sub-models for service stations located in urban areas and for service

stations located along routes produced a better fit. The models reflect, without exception, the

importance of the variables representing the size of service station and the traffic passing by,

as well as variables which express location and road type. The fast growth experienced by this

market explains the lesser impact on sales of the area sales potential and of competition.


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CHAPTER TWO

2.0 DATA COLLECTION

2.1 METHOD OF DATA COLLECTION

The data collected is a raw or primary data. The data was collected by method of Registration.

The statistics of sales are taken immediately they are made.

2.2 DATA COLLECTED

The products whose sales were considered in this research are:

2.2.1 COCA COLA

Coca-Cola is the most popular and biggest-selling soft drink in history. An icon of all times,

Coca-Cola is the best-known product in the world.

Created in Atlanta, Georgia, by Dr. John S. Pemberton, Coca-Cola was first offered as a fountain

beverage by mixing Coca-Cola syrup with carbonated water. Coca-Cola was introduced in 1886,

patented in 1887, registered as a trademark in 1893 and by 1895 it was being sold in every state

and territory in the United States. In 1899, The Coca-Cola Company began franchised bottling

operations in and outside the United States taking Coca-Cola to consumers in other parts of

North America and Europe and in subsequent years to other parts of the world.

In 1951, the refreshing wave of Coca-Cola arrived in Nigeria and has remained a hit with

consumers across the country.

Coca-Cola is available in: 35cl and 50cl classic glass contour bottle; 33cl on-the-go Can, 50cl

and 1.5L PET bottle.

Coca-cola is made with Carbonated water, sugar, Carbon-dioxide, caffeine, Phosphoric acid,

caramel color and flavoring.


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2.2.2 FANTA

Fanta is a bubbly, fruity, and colorful sparkling drink. The brand represents a playful spirit;

appealing to everyone to laugh, play and take life less seriously.

Launched in 1960, Fanta is the second oldest brand of The Coca-Cola Company in Nigeria.Fanta is enjoyed more than 130 million times everyday worldwide.

In Nigeria, Fanta is available in orange and pineapple flavors.

Fanta is available in: 35cl and 50cl glass bottle; 33cl on-the-go Can and 50cl PET bottle.

Fanta is made of Carbonated water, sugar, citric acid, ascorbic acid, stabilizers, flavorings,

sodium benzoate and colouring.

2.2.3 SPRITE

Sprite is the world's leading lemon-lime flavored soft drink. It is sold in more than 190 countries

including Nigeria and ranks as the No. 4 soft drink worldwide. With a strong appeal to young

people, Sprite is often perceived as the better for you Sparkling Beverage because of its clear

color, caffeine-free, and naturally flavored formula.

Packaged in the familiar green dimpled bottle with the shoulders, millions of people enjoy Sprite

because of its crisp, clean taste that really quenches your thirst. Sprite also has an honest,

straightforward attitude that sets it apart from other soft drinks and offers a total freedom from

thirst.

To appeal to its large youthful consumer base as well as bring out the creativity and ingenuity inthis group, Sprite has engaged in the Sprite Triple Slam activation. The Sprite Triple Slam is a

property of the Sprite brand that combines three fun elements of basketball, music and dance..

Sprite is available in: 35cl and 50cl glass bottle; 33cl on-the-go Can and 50cl PET bottle.

Sprite is made of Carbonated water, sugar, citric acid, flavorings, sodium salt, and sodium

benzoate.

http://www.nigerianbottlingcompanyltd.com/Productsandbrands/Sparklingbeverages
http://www.nigerianbottlingcompanyltd.com/Productsandbrands/Sparklingbeverageshttp://www.nigerianbottlingcompanyltd.com/Productsandbrands/Sparklingbeverageshttp://www.nigerianbottlingcompanyltd.com/Productsandbrands/Sparklingbeverages


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CHAPTER THREE

3.0 DATA ANALYSIS

3.1 Time Series

3.1.1 Coke

Year Variable (Coke) YEAR_ MONTH_ DATE_ Pred._Coke Forecast

2001 3163820 2001 1 Jan-01

2001 3163367 2001 2 Feb-01 3164555

2001 3163453 2001 3 Mar-01 3164546

2001 3164120 2001 4 Apr-01 3164711

2001 3165928 2001 5 May-01 3165196

2001 3165387 2001 6 Jun-01 3166426

2001 3168931 2001 7 Jul-01 3166645

2001 3168740 2001 8 Aug-01 3168782

2001 3169621 2001 9 Sep-01 3169545

2001 3169541 2001 10 Oct-01 3170452

2001 3169897 2001 11 Nov-01 3170827

2001 3170620 2001 12 Dec-01 3171236

2002 3175360 2002 1 Jan-02 3171641

2002 3177768 2002 2 Feb-02 3174468

2002 3178135 2002 3 Mar-02 3176950

2002 3176260 2002 4 Apr-02 3178275

2002 3181135 2002 5 May-02 3177896

2002 3180497 2002 6 Jun-02 3180558

2002 3183760 2002 7 Jul-02 3181251

2002 3187690 2002 8 Aug-02 3183479

2002 3188532 2002 9 Sep-02 3186602

2002 3188654 2002 10 Oct-02 31884172002 3185135 2002 11 Nov-02 3189368

2002 3189708 2002 12 Dec-02 3187900

2003 3193880 2003 1 Jan-03 3189749

2003 3191497 2003 2 Feb-03 3192741

2003 3196888 2003 3 Mar-03 3192676

2003 3193135 2003 4 Apr-03 3195819


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2003 3193366 2003 5 May-03 3194987

2003 3199897 2003 6 Jun-03 3194956

2003 3195808 2003 7 Jul-03 3198579

2003 3195497 2003 8 Aug-03 3197729

2003 3198906 2003 9 Sep-03 31974092003 3197875 2003 10 Oct-03 3199181

2003 3199497 2003 11 Nov-03 3199299

2003 3201135 2003 12 Dec-03 3200346

2004 3190357 2004 1 Jan-04 3201501

2004 3199876 2004 2 Feb-04 3196005

2004 3199535 2004 3 Mar-04 3199238

2004 3200780 2004 4 Apr-04 3200080

2004 3202551 2004 5 May-04 3201240

2004 3203876 2004 6 Jun-04 3202751

2004 3206686 2004 7 Jul-04 3204156

2004 3206950 2004 8 Aug-04 3206364

2004 3205551 2004 9 Sep-04 3207465

2004 3205880 2004 10 Oct-04 3207245

2004 3205135 2004 11 Nov-04 3207424

2004 3204632 2004 12 Dec-04 3207093

2005 3203865 2005 1 Jan-05 3206511

2005 3200880 2005 2 Feb-05 3205849

2005 3204689 2005 3 Mar-05 3203916

2005 3204889 2005 4 Apr-05 3205266

2005 3206807 2005 5 May-05 3205828

2005 3208557 2005 6 Jun-05 3207185

2005 3208805 2005 7 Jul-05 3208740

2005 3207935 2005 8 Aug-05 3209569

2005 3210651 2005 9 Sep-05 3209505

2005 3212975 2005 10 Oct-05 3211059

2005 3209555 2005 11 Nov-05 3212980

2006 3209664 2005 12 Dec-05 3211915

2006 3211888 2006 1 Jan-06 3211476

2006 3211552 2006 2 Feb-06 3212513

2006 3214880 2006 3 Mar-06 3212722

2006 3213897 2006 4 Apr-06 3214720

2006 3215135 2006 5 May-06 3214985

2006 3215659 2006 6 Jun-06 3215890


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2006 3216680 2006 7 Jul-06 3216577

2006 3211550 2006 8 Aug-06 3217476

2006 3214897 2006 9 Sep-06 3215023

2006 3215686 2006 10 Oct-06 3215994

2006 3215875 2006 11 Nov-06 32167222006 3218551 2006 12 Dec-06 3217161

2007 3226812 2007 1 Jan-07 3218680

2007 3226543 2007 2 Feb-07 3223906

2007 3226663 2007 3 Mar-07 3225886

2007 3226098 2007 4 Apr-07 3226994

2007 3227076 2007 5 May-07 3227251

2007 3227387 2007 6 Jun-07 3227979

2007 3228965 2007 7 Jul-07 3228474

2007 3228987 2007 8 Aug-07 3229600

2007 3228675 2007 9 Sep-07 3230097

2007 3230543 2007 10 Oct-07 3230190

2007 3232654 2007 11 Nov-07 3231314

2007 3235645 2007 12 Dec-07 3232954

2008 3237980 2008 1 Jan-08 3235132

2008 3234790 2008 2 Feb-08 3237372

2008 3230765 2008 3 Mar-08 3236597

2008 3231843 2008 4 Apr-08 3234186

2008 3235675 2008 5 May-08 3233842

2008 3233489 2008 6 Jun-08 3235745

2008 3232743 2008 7 Jul-08 3235258

2008 3237611 2008 8 Aug-08 3234755

2008 3239543 2008 9 Sep-08 3237275

2008 3238563 2008 10 Oct-08 3239328

2008 3238097 2008 11 Nov-08 3239714

2008 3240231 2008 12 Dec-08 3239732

2009 3242563 2009 1 Jan-09 3240777

2009 3243795 2009 2 Feb-09 3242496

2009 3241897 2009 3 Mar-09 3243918

2009 3239780 2009 4 Apr-09 3243519

2009 3237679 2009 5 May-09 3242278

2009 3239670 2009 6 Jun-09 3240631

2009 3240080 2009 7 Jul-09 3241056

2009 3241780 2009 8 Aug-09 3241385


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2009 3240900 2009 9 Sep-09 3242487

2009 3243650 2009 10 Oct-09 3242461

2009 3243460 2009 11 Nov-09 3244054

2009 3244674 2009 12 Dec-09 3244592

2010 3238800 2010 1 Jan-10 32453722010 3237908 2010 2 Feb-10 3242445

2010 3241765 2010 3 Mar-10 3240862

2010 3242670 2010 4 Apr-10 3242279

2010 3244000 2010 5 May-10 3243265

2010 3246443 2010 6 Jun-10 3244464

2010 3235330 2010 7 Jul-10 3246362

2010 3240200 2010 8 Aug-10 3240992

2010 3242587 2010 9 Sep-10 3241721

2010 3243800 2010 10 Oct-10 3243116

2010 3240180 2010 11 Nov-10 3244359

2010 3244660 2010 12 Dec-10 3242911

2011 3245856 2011 1 Jan-11 3244725

2011 3248850 2011 2 Feb-11 3246047

2011 3254640 2011 3 Mar-11 3248324

2011 3252675 2011 4 Apr-11 3252526

2011 3256800 2011 5 May-11 3253204

2011 3251000 2011 6 Jun-11 3255992

2011 3257540 2011 7 Jul-11 3253923

2011 3259430 2011 8 Aug-11 3256912

2011 3258440 2011 9 Sep-11 3259069

2011 3261780 2011 10 Oct-11 3259506

2011 3264550 2011 11 Nov-11 3261670

2011 3266760 2011 12 Dec-11 3264110

2012 3263760 2012 1 Jan-12 3266216

2012 3268540 2012 2 Feb-12 3265500

2012 3265769 2012 3 Mar-12 3268012

2012 3270896 2012 4 Apr-12 3267446

2012 3269123 2012 5 May-12 3270213

2012 3266432 2012 6 Jun-12 3270310

2012 3274436 2012 7 Jul-12 3268994

2012 3272890 2012 8 Aug-12 3272975

2012 3277006 2012 9 Sep-12 3273627

2012 3278590 2012 10 Oct-12 3276370


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2012 3274360 2012 11 Nov-12 3278391

2012 3280684 2012 12 Dec-12 3276975

2013 Jan-13 3279010

2013 Feb-13 3297889

2013 Mar-13 3304216

2013 Apr-13 3303210

2103 May-13 3315400

2013 Jun-13 3315650

2013 Jul-13 3304235

2013 Aug-13 3314872

2013 Sep-13 3315785

2013 Oct-13 3318460

2013 Nov-13 3320078

2013 Dec-13 3320050


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Autocorrelations

Series: Coke

Lag Autocorrelation Std. Errora Box-Ljung Statistic

Value df Sig.b

1 .968 .082 137.664 1 .000

2 .941 .082 268.805 2 .000

3 .912 .082 392.945 3 .000

4 .883 .082 510.124 4 .000

5 .858 .081 621.381 5 .000

6 .829 .081 726.091 6 .000

7 .805 .081 825.487 7 .000

8 .780 .080 919.483 8 .000

9 .753 .080 1007.873 9 .000

10 .728 .080 1090.973 10 .000

11 .701 .080 1168.586 11 .000

12 .674 .079 1241.033 12 .000

13 .649 .079 1308.614 13 .000

14 .625 .079 1371.870 14 .000

15 .601 .078 1430.809 15 .000

16 .579 .078 1485.817 16 .000

a. The underlying process assumed is independence (white noise).

b. Based on the asymptotic chi-square approximation.


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Partial Autocorrelations

Series: Coke

Lag Partial Autocorrelation Std. Error

1 .968 .083

2 .075 .083

3 -.040 .083

4 -.027 .083

5 .038 .083

6 -.051 .083

7 .044 .083

8 -.014 .083

9 -.038 .083

10 -.010 .083

11 -.028 .083

12 -.012 .083

13 .001 .083

14 .020 .08315 -.024 .083

16 .007 .083


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Model Description

Model Type

Model ID Coke Model_1 ARIMA(1,1,1)(0,0,0)

Model Summary

Fit Statistic Coke-Model_1

Stationary R-squared 0.166

R-squared 0.991

RMSE 2873.385

MAPE 0.0625

MAE 2102.934

MaxAPE 0.349

MaxAE 11143.73

Normalized BIC 16.065

Statistics 13.068

DF 16

Sig. 0.668


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3.1.2 Fanta

Year

Variable

(Fanta) YEAR_ MONTH_ DATE_ Pred._Fanta Forecast

2001 3163135 2001 1 Jan-012001 3163420 2001 2 Feb-01 3163918

2001 3163576 2001 3 Mar-01 3164323

2001 3164345 2001 4 Apr-01 3164599

2001 3164490 2001 5 May-01 3165249

2001 3165686 2001 6 Jun-01 3165516

2001 3167467 2001 7 Jul-01 3166443

2001 3165476 2001 8 Aug-01 3167872

2001 3168139 2001 9 Sep-01 3166871

2001 3168432 2001 10 Oct-01 31686242001 3169453 2001 11 Nov-01 3169131

2001 3169420 2001 12 Dec-01 3170039

2002 3171131 2002 1 Jan-02 3170382

2002 3174776 2002 2 Feb-02 3171720

2002 3172709 2002 3 Mar-02 3174521

2002 3173766 2002 4 Apr-02 3173773

2002 3173135 2002 5 May-02 3174604

2002 3172708 2002 6 Jun-02 3174372

2002 3175765 2002 7 Jul-02 3174108

2002 3175740 2002 8 Aug-02 3176141

2002 3178135 2002 9 Sep-02 3176490

2002 3179735 2002 10 Oct-02 3178326

2002 3179689 2002 11 Nov-02 3179843

2002 3185761 2002 12 Dec-02 3180267

2003 3185576 2003 1 Jan-03 3184768

2003 3187880 2003 2 Feb-03 3185618

2003 3189135 2003 3 Mar-03 3187730

2003 3189808 2003 4 Apr-03 3189200

2003 3191646 2003 5 May-03 3190179

2003 3190874 2003 6 Jun-03 3191819

2003 3193821 2003 7 Jul-03 3191756

2003 3193576 2003 8 Aug-03 3193927

2003 3192850 2003 9 Sep-03 3194237

2003 3195545 2003 10 Oct-03 3193985


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2003 3194556 2003 11 Nov-03 3195860

2003 3196880 2003 12 Dec-03 3195557

2004 3196881 2004 1 Jan-04 3197334

2004 3194616 2004 2 Feb-04 3197717

2004 3195821 2004 3 Mar-04 31963942004 3196477 2004 4 Apr-04 3197039

2004 3198880 2004 5 May-04 3197534

2004 3199135 2004 6 Jun-04 3199277

2004 3199497 2004 7 Jul-04 3199821

2004 3198800 2004 8 Aug-04 3200313

2004 3196616 2004 9 Sep-04 3200024

2004 3197397 2004 10 Oct-04 3198549

2004 3199820 2004 11 Nov-04 3198794

2004 3202487 2004 12 Dec-04 3200370

2005 3204803 2005 1 Jan-05 3202565

2005 3201835 2005 2 Feb-05 3204678

2005 3200580 2005 3 Mar-05 3203271

2005 3200386 2005 4 Apr-05 3202382

2005 3201277 2005 5 May-05 3202059

2005 3202770 2005 6 Jun-05 3202520

2005 3202697 2005 7 Jul-05 3203560

2005 3201806 2005 8 Aug-05 3203713

2005 3205675 2005 9 Sep-05 3203210

2005 3204635 2005 10 Oct-05 3205781

2005 3205785 2005 11 Nov-05 3205536

2006 3205907 2005 12 Dec-05 3206449

2006 3206750 2006 1 Jan-06 3206865

2006 3207900 2006 2 Feb-06 3207614

2006 3207455 2006 3 Mar-06 3208604

2006 3206487 2006 4 Apr-06 3208567

2006 3203760 2006 5 May-06 3208000

2006 3205880 2006 6 Jun-06 3206064

2006 3208437 2006 7 Jul-06 3207096

2006 3209110 2006 8 Aug-06 3208864

2006 3209155 2006 9 Sep-06 3209667

2006 3210227 2006 10 Oct-06 3209983

2006 3210650 2006 11 Nov-06 3210879

2006 3211875 2006 12 Dec-06 3211399


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2007 3216786 2007 1 Jan-07 3212531

2007 3216453 2007 2 Feb-07 3216173

2007 3216509 2007 3 Mar-07 3216759

2007 3217378 2007 4 Apr-07 3217226

2007 3219780 2007 5 May-07 32180712007 3219600 2007 6 Jun-07 3219962

2007 3218480 2007 7 Jul-07 3220299

2007 3217670 2007 8 Aug-07 3219777

2007 3218650 2007 9 Sep-07 3219214

2007 3219400 2007 10 Oct-07 3219761

2007 3218450 2007 11 Nov-07 3220322

2007 3219440 2007 12 Dec-07 3219797

2008 3219352 2008 1 Jan-08 3220524

2008 3221350 2008 2 Feb-08 3220588

2008 3222210 2008 3 Mar-08 3222002

2008 3220900 2008 4 Apr-08 3222875

2008 3223780 2008 5 May-08 3222258

2008 3219564 2008 6 Jun-08 3224202

2008 3222807 2008 7 Jul-08 3221694

2008 3222576 2008 8 Aug-08 3223554

2008 3221786 2008 9 Sep-08 3223611

2008 3220090 2008 10 Oct-08 3223162

2008 3220800 2008 11 Nov-08 3221950

2008 3219670 2008 12 Dec-08 3222170

2009 3218560 2009 1 Jan-09 3221445

2009 3220543 2009 2 Feb-09 3220530

2009 3222890 2009 3 Mar-09 3221633

2009 3232000 2009 4 Apr-09 3223338

2009 3230760 2009 5 May-09 3229902

2009 3231098 2009 6 Jun-09 3230459

2009 3223000 2009 7 Jul-09 3231347

2009 3229432 2009 8 Aug-09 3226261

2009 3228450 2009 9 Sep-09 3229821

2009 3234540 2009 10 Oct-09 3229506

2009 3235567 2009 11 Nov-09 3233724

2009 3231890 2009 12 Dec-09 3235257

2010 3230700 2010 1 Jan-10 3233488

2010 3232450 2010 2 Feb-10 3232591


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2010 3240060 2010 3 Mar-10 3233568

2010 3236890 2010 4 Apr-10 3238843

2010 3239098 2010 5 May-10 3237733

2010 3237607 2010 6 Jun-10 3239431

2010 3238555 2010 7 Jul-10 32388132010 3241606 2010 8 Aug-10 3239491

2010 3243060 2010 9 Sep-10 3241704

2010 3247380 2010 10 Oct-10 3243166

2010 3248600 2010 11 Nov-10 3246569

2010 3253437 2010 12 Dec-10 3248232

2011 3251700 2011 1 Jan-11 3252281

2011 3260807 2011 2 Feb-11 3252141

2011 3263808 2011 3 Mar-11 3258720

2011 3257354 2011 4 Apr-11 3262173

2011 3261200 2011 5 May-11 3258999

2011 3259860 2011 6 Jun-11 3261482

2011 3254909 2011 7 Jul-11 3260989

2011 3266010 2011 8 Aug-11 3257683

2011 3264110 2011 9 Sep-11 3264621

2011 3268065 2011 10 Oct-11 3264411

2011 3255650 2011 11 Nov-11 3267485

2011 3256120 2011 12 Dec-11 3259774

2012 3260165 2012 1 Jan-12 3259008

2012 3257760 2012 2 Feb-12 3261145

2012 3259450 2012 3 Mar-12 3259668

2012 3258895 2012 4 Apr-12 3260590

2012 3264125 2012 5 May-12 3260292

2012 3264560 2012 6 Jun-12 3263819

2012 3263340 2012 7 Jul-12 3264775

2012 3260320 2012 8 Aug-12 3264383

2012 3262111 2012 9 Sep-12 3262420

2012 3266560 2012 10 Oct-12 3263291

2012 3269808 2012 11 Nov-12 3266341

2012 3268900 2012 12 Dec-12 3269141

2013 Jan-13 3269104

2013 Feb-13 3278807

2013 Mar-13 3269750

2013 Apr-13 3270033

2103 May-13 3269998


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Page 22of 36

2013 Jun-13 3272430

2013 Jul-13 3273350

2013 Aug-13 3273664

2013 Sep-13 3269880

2013 Oct-13 3271876

2013 Nov-13 3273452

2013 Dec-13 3276380


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Autocorrelations

Series: Fanta

Lag Autocorrelation Std. Errora Box-Ljung Statistic

Value df Sig.b

1 .972 .082 138.953 1 .000

2 .946 .082 271.479 2 .000

3 .921 .082 397.835 3 .000

4 .898 .082 519.063 4 .000

5 .877 .081 635.509 5 .000

6 .853 .081 746.289 6 .000

7 .828 .081 851.587 7 .000

8 .801 .080 950.808 8 .000

9 .778 .080 1045.094 9 .000

10 .755 .080 1134.426 10 .000

11 .732 .080 1219.034 11 .000

12 .707 .079 1298.700 12 .000

13 .685 .079 1373.977 13 .000

14 .663 .079 1445.161 14 .000

15 .636 .078 1511.041 15 .000

16 .609 .078 1571.916 16 .000

a. The underlying process assumed is independence (white noise).

b. Based on the asymptotic chi-square approximation.


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Series: Fanta


1 .972 .083

2 .018 .083

3 -.003 .083

4 .051 .083

5 .011 .083

6 -.075 .083

7 -.007 .083

8 -.063 .083

9 .051 .083

10 -.021 .083

11 -.004 .083

12 -.033 .083

13 .030 .083

14 -.003 .083

15 -.125 .083

16 -.015 .083


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Model Description

Model Type

Model ID Fanta Model_1 ARIMA(1,1,1)(0,0,0)

Model Summary

Fit Statistic Fanta-Model_1


R-squared 0.991

RMSE 2776.094

MAPE 0.059

MAE 1896.053

MaxAPE 0.367

MaxAE 11956,841


Statistics 19.826

DF 16

Sig. 0.228


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3.1.3 Sprite

Year

Variable

(Sprite) YEAR_ MONTH_ DATE_ Pred._Sprite Forecast

2001 3162489 2001 1 Jan-01

2001 3162463 2001 2 Feb-01 3163226

2001 3164414 2001 3 Mar-01 3163565

2001 3164935 2001 4 Apr-01 3164642

2001 3164520 2001 5 May-01 3165507

2001 3165429 2001 6 Jun-01 3165940

2001 3165609 2001 7 Jul-01 3166521

2001 3165486 2001 8 Aug-01 3166988

2001 3165135 2001 9 Sep-01 3167268

2001 3167420 2001 10 Oct-01 3167344

2001 3168690 2001 11 Nov-01 3168118

2001 3168410 2001 12 Dec-01 3169086

2002 3170135 2002 1 Jan-02 3169612

2002 3171760 2002 2 Feb-02 3170505

2002 3170135 2002 3 Mar-02 3171652

2002 3172755 2002 4 Apr-02 3171935

2002 3173776 2002 5 May-02 3172925

2002 3173656 2002 6 Jun-02 3173963

2002 3174760 2002 7 Jul-02 3174639

2002 3174497 2002 8 Aug-02 3175438

2002 3175135 2002 9 Sep-02 3175912

2002 3175709 2002 10 Oct-02 3176427

2002 3176768 2002 11 Nov-02 3176969

2002 3176737 2002 12 Dec-02 3177680

2003 3179880 2003 1 Jan-03 3178110

2003 3179135 2003 2 Feb-03 3179395

2003 3182686 2003 3 Mar-03 3180080

2003 3184576 2003 4 Apr-03 3181649

2003 3184875 2003 5 May-03 3183366

2003 3188497 2003 6 Jun-03 31846422003 3189840 2003 7 Jul-03 3186646

2003 3187135 2003 8 Aug-03 3188480

2003 3186860 2003 9 Sep-03 3188869

2003 3189990 2003 10 Oct-03 3188987

2003 3190764 2003 11 Nov-03 3190057

2003 3190550 2003 12 Dec-03 3191081


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2004 3193135 2004 1 Jan-04 3191655

2004 3193880 2004 2 Feb-04 3192853

2004 3194865 2004 3 Mar-04 3193942

2004 3193357 2004 4 Apr-04 3194995

2004 3195684 2004 5 May-04 3195240

2004 3195353 2004 6 Jun-04 3196113

2004 3198831 2004 7 Jul-04 3196640

2004 3196811 2004 8 Aug-04 3198090

2004 3197445 2004 9 Sep-04 3198486

2004 3199180 2004 10 Oct-04 3198912

2004 3197657 2004 11 Nov-04 3199763

2004 3195886 2004 12 Dec-04 3199884

2005 3199980 2005 1 Jan-05 3199318

2005 3198557 2005 2 Feb-05 3200208

2005 3197878 2005 3 Mar-05 3200437

2005 3199876 2005 4 Apr-05 3200348

2005 3201229 2005 5 May-05 3200913

2005 3202865 2005 6 Jun-05 3201763

2005 3205357 2005 7 Jul-05 3202878

2005 3205686 2005 8 Aug-05 3204446

2005 3209880 2005 9 Sep-05 3205645

2005 3206812 2005 10 Oct-05 3207781

2005 3203357 2005 11 Nov-05 3208314

2006 3204540 2005 12 Dec-05 3207512

2006 3209660 2006 1 Jan-06 3207230

2006 3205535 2006 2 Feb-06 3208697

2006 3206780 2006 3 Mar-06 3208471

2006 3207980 2006 4 Apr-06 3208635

2006 3204586 2006 5 May-06 3209155

2006 3204457 2006 6 Jun-06 3208453

2006 3206340 2006 7 Jul-06 3207881

2006 3206520 2006 8 Aug-06 3208102

2006 3209125 2006 9 Sep-06 3208350

2006 3210230 2006 10 Oct-06 3209349

2006 3210447 2006 11 Nov-06 32104202006 3212886 2006 12 Dec-06 3211226

2007 3216467 2007 1 Jan-07 3212485

2007 3216456 2007 2 Feb-07 3214509

2007 3216476 2007 3 Mar-07 3215925

2007 3216990 2007 4 Apr-07 3216873

2007 3216500 2007 5 May-07 3217669


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2007 3216443 2007 6 Jun-07 3218056

2007 3216760 2007 7 Jul-07 3218288

2007 3216550 2007 8 Aug-07 3218546

2007 3216640 2007 9 Sep-07 3218662

2007 3218490 2007 10 Oct-07 3218768

2007 3218644 2007 11 Nov-07 3219432

2007 3217456 2007 12 Dec-07 3219963

2008 3218432 2008 1 Jan-08 3219889

2008 3218670 2008 2 Feb-08 3220128

2008 3219750 2008 3 Mar-08 3220387

2008 3219550 2008 4 Apr-08 3220911

2008 3220320 2008 5 May-08 3221222

2008 3221564 2008 6 Jun-08 3221673

2008 3220555 2008 7 Jul-08 3222387

2008 3222342 2008 8 Aug-08 3222570

2008 3224890 2008 9 Sep-08 3223242

2008 3223780 2008 10 Oct-08 3224537

2008 3226890 2008 11 Nov-08 3225101

2008 3225765 2008 12 Dec-08 3226442

2009 3224654 2009 1 Jan-09 3226985

2009 3228650 2009 2 Feb-09 3226972

2009 3228908 2009 3 Mar-09 3228213

2009 3227098 2009 4 Apr-09 3229205

2009 3229780 2009 5 May-09 3229297

2009 3226564 2009 6 Jun-09 3230176

2009 3229556 2009 7 Jul-09 3229799

2009 3234654 2009 8 Aug-09 3230436

2009 3233680 2009 9 Sep-09 3232542

2009 3231675 2009 10 Oct-09 3233739

2009 3229908 2009 11 Nov-09 3233879

2009 3232009 2009 12 Dec-09 3233375

2010 3230350 2010 1 Jan-10 3233617

2010 3243800 2010 2 Feb-10 3233299

2010 3231760 2010 3 Mar-10 3237325

2010 3236000 2010 4 Apr-10 32364582010 3233900 2010 5 May-10 3236981

2010 3229605 2010 6 Jun-10 3236755

2010 3240125 2010 7 Jul-10 3235204

2010 3233590 2010 8 Aug-10 3237429

2010 3239760 2010 9 Sep-10 3237055

2010 3241805 2010 10 Oct-10 3238632


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2010 3246660 2010 11 Nov-10 3240458

2010 3241880 2010 12 Dec-10 3243255

2011 3244990 2011 1 Jan-11 3243639

2011 3252760 2011 2 Feb-11 3244783

2011 3250800 2011 3 Mar-11 3248075

2011 3249870 2011 4 Apr-11 3249800

2011 3256230 2011 5 May-11 3250610

2011 3259850 2011 6 Jun-11 3253149

2011 3249350 2011 7 Jul-11 3256116

2011 3255665 2011 8 Aug-11 3254830

2011 3259065 2011 9 Sep-11 3255766

2011 3266550 2011 10 Oct-11 3257602

2011 3263530 2011 11 Nov-11 3261267

2011 3254238 2011 12 Dec-11 3262898

2012 3269786 2012 1 Jan-12 3260913

2012 3255564 2012 2 Feb-12 3264339

2012 3256768 2012 3 Mar-12 3262427

2012 3267653 2012 4 Apr-12 3261250

2012 3264115 2012 5 May-12 3263954

2012 3267005 2012 6 Jun-12 3264855

2012 3256740 2012 7 Jul-12 3266298

2012 3256553 2012 8 Aug-12 3264063

2012 3269120 2012 9 Sep-12 3262314

2012 3270440 2012 10 Oct-12 3265143

2012 3270559 2012 11 Nov-12 3267703

2012 3272880 2012 12 Dec-12 3269468

2013 Jan-13 3267386

2013 Feb-13 3269665

2013 Mar-13 3267989

2013 Apr-13 3265996

2103 May-13 3267035

2013 Jun-13 3269123

2013 Jul-13 3270541

2013 Aug-13 3268790

2013 Sep-13 32669852013 Oct-13 3269554

2013 Nov-13 3270088

2013 Dec-13 3274320


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Series: Sprite


1 .967 .083

2 .109 .083

3 .031 .083

4 -.043 .083

5 .107 .083

6 -.045 .083

7 -.045 .083

8 -.055 .083

9 -.066 .083

10 .074 .083

11 .063 .083

12 -.156 .083

13 .044 .083

14 -.024 .083

15 -.045 .083

16 -.010 .083


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Page 33of 36

Model Description

Model Type

Model ID Sprite Model_1 ARIMA(1,1,1)(0,0,0)

Model Summary

Fit Statistic Sprite-Model_1


R-squared 0.988

RMSE 3310.33

MAPE 0.072

MAE 2334.801

MaxAPE 0.324

MaxAE 10500.563


Statistics 33.546

DF 16Sig. 0.006


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3.2 Friedman Test

Descriptive Statistics

N Mean Std.

Deviation

Minimum Maximum Percentiles

25th 50th (Median) 75th

Coke 144 3220605.7431 29963.40051 3163367 3280684 3199620.25 3222324.5 3241867.75

Fanta 144 3214567.6806 29374.61634 3163135 3269808 3195614.0 3214164.0 3234017.5

Sprite 144 3213202.0139 29729.55225 3162463 3272880 3191356.75 3214664.5 3231946.75

Ranks

Mean Rank

Coke 2.76Fanta 1.86

Sprite 1.38

Test Statisticsa

N 144

Chi-Square 140.292

df 2

Asymp. Sig. .000

a. Friedman Test

With an asymptotic significant value = .000 < .05the null hypothesis it reject for

the alternate that the sales performance of the products is not equal. This is fatherly

shown by the mean rank indicating that coke and sprite has the highest and least

sales respectively with a mean rank range of 1.38


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CHAPTER FOUR

4.0 SUMMARY, CONCLUSION AND RECCOMMENDATION

Nigeria Bottling Company (NBC) has been a major player in the production of soft

drinks in Nigeria. The study focuses on the sales performance of her major

products (Coke, Fanta and Sprite). In studying these variables, a raw data showing

the sales rates of these products were collected from 20012012.

The analysis was run using SPSS v21.0 with the aim modeling the sales of these

products over the years and making forecast (2013).

CONCLUSION

Analysis showed that although with slight drops, there was a continuous increase

in the sales of the products during the years in question. The model ARIMA (1,1,1)

was developed for althrough for predicting the trend and forecasting subsequent

years. The prediction almost followed the actual sales while the forecast showed

there will be a increase in the sales of the product in 2013.

Further test showed that the sales performances are not equal as the product coke

led the way.

RECOMMENDATION

The rise shown in the forecast is not convincing and as such, the branch of NBC

considered should improve in the stability of the sales of her product. Also, advert

placement for both fanta and sprite should be considered to foster a health

competition between the products.

Date post:	02-Jun-2018
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